Overview

Dataset statistics

Number of variables39
Number of observations46927
Missing cells243508
Missing cells (%)13.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 MiB
Average record size in memory298.0 B

Variable types

Numeric12
DateTime5
Text5
Categorical15
Boolean2

Alerts

no_of_adults is highly overall correlated with no_of_roomHigh correlation
no_of_room is highly overall correlated with no_of_adultsHigh correlation
language is highly overall correlated with original_payment_currencyHigh correlation
original_payment_currency is highly overall correlated with languageHigh correlation
accommadation_type_name is highly imbalanced (64.5%)Imbalance
no_of_extra_bed is highly imbalanced (96.1%)Imbalance
original_payment_method is highly imbalanced (58.3%)Imbalance
original_payment_type is highly imbalanced (91.4%)Imbalance
request_latecheckin is highly imbalanced (82.6%)Imbalance
request_airport is highly imbalanced (93.7%)Imbalance
request_earlycheckin is highly imbalanced (78.8%)Imbalance
request_nonesmoke has 20030 (42.7%) missing valuesMissing
request_latecheckin has 20030 (42.7%) missing valuesMissing
request_highfloor has 20030 (42.7%) missing valuesMissing
request_largebed has 20030 (42.7%) missing valuesMissing
request_twinbeds has 20030 (42.7%) missing valuesMissing
request_airport has 20030 (42.7%) missing valuesMissing
request_earlycheckin has 20030 (42.7%) missing valuesMissing
hotel_brand_code has 34699 (73.9%) missing valuesMissing
hotel_chain_code has 34343 (73.2%) missing valuesMissing
cancellation_datetime has 34250 (73.0%) missing valuesMissing
original_selling_amount is highly skewed (γ1 = 36.55009602)Skewed
h_booking_id has unique valuesUnique
hotel_star_rating has 2060 (4.4%) zerosZeros
no_of_children has 42690 (91.0%) zerosZeros

Reproduction

Analysis started2023-06-07 20:00:00.748945
Analysis finished2023-06-07 20:00:41.154730
Duration40.41 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

h_booking_id
Real number (ℝ)

Distinct46927
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.7482761 × 1016
Minimum-9.2231941 × 1018
Maximum9.2233383 × 1018
Zeros0
Zeros (%)0.0%
Negative23545
Negative (%)50.2%
Memory size366.7 KiB
2023-06-07T23:00:41.258476image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-9.2231941 × 1018
5-th percentile-8.3231332 × 1018
Q1-4.6146133 × 1018
median-3.2064787 × 1016
Q34.5760991 × 1018
95-th percentile8.2846133 × 1018
Maximum9.2233383 × 1018
Range-2.1169456 × 1014
Interquartile range (IQR)9.1907124 × 1018

Descriptive statistics

Standard deviation5.3247643 × 1018
Coefficient of variation (CV)-304.57227
Kurtosis-1.1991323
Mean-1.7482761 × 1016
Median Absolute Deviation (MAD)4.5955943 × 1018
Skewness0.0033792406
Sum-8.756789 × 1018
Variance2.8353114 × 1037
MonotonicityNot monotonic
2023-06-07T23:00:41.430344image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.861445259 × 10181
 
< 0.1%
-5.859385366 × 10181
 
< 0.1%
4.102580392 × 10181
 
< 0.1%
1.795941529 × 10181
 
< 0.1%
-5.721949141 × 10181
 
< 0.1%
-4.284526584 × 10181
 
< 0.1%
2.103613272 × 10181
 
< 0.1%
5.236811047 × 10181
 
< 0.1%
-2.3832694 × 10181
 
< 0.1%
-2.788888768 × 10181
 
< 0.1%
Other values (46917) 46917
> 99.9%
ValueCountFrequency (%)
-9.223194056 × 10181
< 0.1%
-9.222713784 × 10181
< 0.1%
-9.222411208 × 10181
< 0.1%
-9.222220846 × 10181
< 0.1%
-9.220467519 × 10181
< 0.1%
-9.219746746 × 10181
< 0.1%
-9.219730436 × 10181
< 0.1%
-9.219588531 × 10181
< 0.1%
-9.219312947 × 10181
< 0.1%
-9.219139934 × 10181
< 0.1%
ValueCountFrequency (%)
9.223338324 × 10181
< 0.1%
9.223221736 × 10181
< 0.1%
9.222651807 × 10181
< 0.1%
9.222015612 × 10181
< 0.1%
9.221958225 × 10181
< 0.1%
9.221798878 × 10181
< 0.1%
9.221611986 × 10181
< 0.1%
9.221457559 × 10181
< 0.1%
9.220807687 × 10181
< 0.1%
9.220787994 × 10181
< 0.1%
Distinct41260
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Minimum2017-07-02 18:58:00
Maximum2018-09-30 10:32:00
2023-06-07T23:00:41.620066image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:41.788345image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct105
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Minimum2018-06-07 00:00:00
Maximum2018-09-29 00:00:00
2023-06-07T23:00:41.970839image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:42.147766image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Minimum2018-07-02 00:00:00
Maximum2018-09-30 00:00:00
2023-06-07T23:00:42.325231image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:42.660398image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

hotel_id
Real number (ℝ)

Distinct24969
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1341712.9
Minimum1
Maximum5823993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:42.835793image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10917.6
Q1255829
median798535
Q32284662
95-th percentile4168202
Maximum5823993
Range5823992
Interquartile range (IQR)2028833

Descriptive statistics

Standard deviation1361519.7
Coefficient of variation (CV)1.0147624
Kurtosis0.077266214
Mean1341712.9
Median Absolute Deviation (MAD)675419
Skewness1.0483226
Sum6.296256 × 1010
Variance1.853736 × 1012
MonotonicityNot monotonic
2023-06-07T23:00:42.992030image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6452 273
 
0.6%
461790 54
 
0.1%
304294 46
 
0.1%
3080111 42
 
0.1%
337607 36
 
0.1%
185945 34
 
0.1%
1603143 34
 
0.1%
43450 33
 
0.1%
4426497 32
 
0.1%
1270365 32
 
0.1%
Other values (24959) 46311
98.7%
ValueCountFrequency (%)
1 2
< 0.1%
16 2
< 0.1%
75 2
< 0.1%
85 1
 
< 0.1%
99 3
< 0.1%
122 2
< 0.1%
140 1
 
< 0.1%
148 1
 
< 0.1%
153 1
 
< 0.1%
168 4
< 0.1%
ValueCountFrequency (%)
5823993 1
< 0.1%
5808200 1
< 0.1%
5799579 1
< 0.1%
5798032 1
< 0.1%
5797970 1
< 0.1%
5795465 1
< 0.1%
5790845 1
< 0.1%
5785518 1
< 0.1%
5771209 1
< 0.1%
5761104 1
< 0.1%
Distinct125
Distinct (%)0.3%
Missing4
Missing (%)< 0.1%
Memory size366.7 KiB
2023-06-07T23:00:43.232514image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters93846
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)< 0.1%

Sample

1st rowAU
2nd rowJP
3rd rowTW
4th rowTR
5th rowJP
ValueCountFrequency (%)
jp 7357
15.7%
th 6331
13.5%
my 6136
13.1%
tw 4062
 
8.7%
id 2883
 
6.1%
kr 2718
 
5.8%
ph 2128
 
4.5%
vn 2105
 
4.5%
us 1445
 
3.1%
hk 1295
 
2.8%
Other values (115) 10463
22.3%
2023-06-07T23:00:43.587982image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 11398
12.1%
H 10432
11.1%
P 9669
 
10.3%
J 7410
 
7.9%
M 6873
 
7.3%
Y 6162
 
6.6%
K 4676
 
5.0%
N 4205
 
4.5%
W 4086
 
4.4%
I 4060
 
4.3%
Other values (16) 24875
26.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 93846
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 11398
12.1%
H 10432
11.1%
P 9669
 
10.3%
J 7410
 
7.9%
M 6873
 
7.3%
Y 6162
 
6.6%
K 4676
 
5.0%
N 4205
 
4.5%
W 4086
 
4.4%
I 4060
 
4.3%
Other values (16) 24875
26.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 93846
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 11398
12.1%
H 10432
11.1%
P 9669
 
10.3%
J 7410
 
7.9%
M 6873
 
7.3%
Y 6162
 
6.6%
K 4676
 
5.0%
N 4205
 
4.5%
W 4086
 
4.4%
I 4060
 
4.3%
Other values (16) 24875
26.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93846
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 11398
12.1%
H 10432
11.1%
P 9669
 
10.3%
J 7410
 
7.9%
M 6873
 
7.3%
Y 6162
 
6.6%
K 4676
 
5.0%
N 4205
 
4.5%
W 4086
 
4.4%
I 4060
 
4.3%
Other values (16) 24875
26.5%
Distinct17476
Distinct (%)37.2%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Minimum1999-09-09 00:00:00
Maximum2019-04-18 10:17:00
2023-06-07T23:00:43.749659image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:43.904487image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

hotel_star_rating
Real number (ℝ)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2239968
Minimum-1
Maximum5
Zeros2060
Zeros (%)4.4%
Negative1
Negative (%)< 0.1%
Memory size366.7 KiB
2023-06-07T23:00:44.036379image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1722628
Coefficient of variation (CV)0.36360543
Kurtosis0.72713638
Mean3.2239968
Median Absolute Deviation (MAD)1
Skewness-0.79401111
Sum151292.5
Variance1.3742
MonotonicityNot monotonic
2023-06-07T23:00:44.144682image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3 14061
30.0%
4 12531
26.7%
5 5053
 
10.8%
2 5018
 
10.7%
3.5 3389
 
7.2%
0 2060
 
4.4%
2.5 1752
 
3.7%
1 1342
 
2.9%
4.5 1174
 
2.5%
1.5 546
 
1.2%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 2060
 
4.4%
1 1342
 
2.9%
1.5 546
 
1.2%
2 5018
 
10.7%
2.5 1752
 
3.7%
3 14061
30.0%
3.5 3389
 
7.2%
4 12531
26.7%
4.5 1174
 
2.5%
ValueCountFrequency (%)
5 5053
 
10.8%
4.5 1174
 
2.5%
4 12531
26.7%
3.5 3389
 
7.2%
3 14061
30.0%
2.5 1752
 
3.7%
2 5018
 
10.7%
1.5 546
 
1.2%
1 1342
 
2.9%
0 2060
 
4.4%
Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Hotel
34255 
Resort
4553 
Guest House / Bed & Breakfast
 
2319
Hostel
 
2075
Serviced Apartment
 
1180
Other values (17)
 
2545

Length

Max length29
Median length5
Mean length6.861146
Min length3

Characters and Unicode

Total characters321973
Distinct characters42
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowResort
2nd rowHotel
3rd rowHotel
4th rowGuest House / Bed & Breakfast
5th rowHotel

Common Values

ValueCountFrequency (%)
Hotel 34255
73.0%
Resort 4553
 
9.7%
Guest House / Bed & Breakfast 2319
 
4.9%
Hostel 2075
 
4.4%
Serviced Apartment 1180
 
2.5%
Apartment 938
 
2.0%
Motel 454
 
1.0%
Resort Villa 278
 
0.6%
Capsule Hotel 270
 
0.6%
Ryokan 238
 
0.5%
Other values (12) 367
 
0.8%

Length

2023-06-07T23:00:44.278742image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hotel 34533
57.1%
resort 4831
 
8.0%
4680
 
7.7%
guest 2319
 
3.8%
house 2319
 
3.8%
bed 2319
 
3.8%
breakfast 2319
 
3.8%
apartment 2118
 
3.5%
hostel 2075
 
3.4%
serviced 1180
 
2.0%
Other values (19) 1775
 
2.9%

Most occurring characters

ValueCountFrequency (%)
e 56086
17.4%
t 50837
15.8%
o 44685
13.9%
H 39053
12.1%
l 38123
11.8%
s 14139
 
4.4%
13541
 
4.2%
r 10616
 
3.3%
a 7968
 
2.5%
R 5069
 
1.6%
Other values (32) 41856
13.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 247574
76.9%
Uppercase Letter 56178
 
17.4%
Space Separator 13541
 
4.2%
Other Punctuation 4680
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 56086
22.7%
t 50837
20.5%
o 44685
18.0%
l 38123
15.4%
s 14139
 
5.7%
r 10616
 
4.3%
a 7968
 
3.2%
u 5006
 
2.0%
d 3539
 
1.4%
k 2631
 
1.1%
Other values (11) 13944
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
H 39053
69.5%
R 5069
 
9.0%
B 4736
 
8.4%
G 2319
 
4.1%
A 2118
 
3.8%
S 1180
 
2.1%
M 454
 
0.8%
V 330
 
0.6%
C 313
 
0.6%
N 195
 
0.3%
Other values (8) 411
 
0.7%
Other Punctuation
ValueCountFrequency (%)
/ 2361
50.4%
& 2319
49.6%
Space Separator
ValueCountFrequency (%)
13541
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 303752
94.3%
Common 18221
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 56086
18.5%
t 50837
16.7%
o 44685
14.7%
H 39053
12.9%
l 38123
12.6%
s 14139
 
4.7%
r 10616
 
3.5%
a 7968
 
2.6%
R 5069
 
1.7%
u 5006
 
1.6%
Other values (29) 32170
10.6%
Common
ValueCountFrequency (%)
13541
74.3%
/ 2361
 
13.0%
& 2319
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 56086
17.4%
t 50837
15.8%
o 44685
13.9%
H 39053
12.1%
l 38123
11.8%
s 14139
 
4.4%
13541
 
4.2%
r 10616
 
3.3%
a 7968
 
2.5%
R 5069
 
1.6%
Other values (32) 41856
13.0%

charge_option
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Pay Now
35335 
Pay Later
11567 
Pay at Check-in
 
25

Length

Max length15
Median length7
Mean length7.4972404
Min length7

Characters and Unicode

Total characters351823
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPay Now
2nd rowPay Now
3rd rowPay Now
4th rowPay Now
5th rowPay Later

Common Values

ValueCountFrequency (%)
Pay Now 35335
75.3%
Pay Later 11567
 
24.6%
Pay at Check-in 25
 
0.1%

Length

2023-06-07T23:00:44.411016image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:44.559622image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
pay 46927
50.0%
now 35335
37.6%
later 11567
 
12.3%
at 25
 
< 0.1%
check-in 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 58519
16.6%
46952
13.3%
P 46927
13.3%
y 46927
13.3%
N 35335
10.0%
o 35335
10.0%
w 35335
10.0%
t 11592
 
3.3%
e 11592
 
3.3%
L 11567
 
3.3%
Other values (8) 11742
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210992
60.0%
Uppercase Letter 93854
26.7%
Space Separator 46952
 
13.3%
Dash Punctuation 25
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 58519
27.7%
y 46927
22.2%
o 35335
16.7%
w 35335
16.7%
t 11592
 
5.5%
e 11592
 
5.5%
r 11567
 
5.5%
h 25
 
< 0.1%
c 25
 
< 0.1%
k 25
 
< 0.1%
Other values (2) 50
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
P 46927
50.0%
N 35335
37.6%
L 11567
 
12.3%
C 25
 
< 0.1%
Space Separator
ValueCountFrequency (%)
46952
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 304846
86.6%
Common 46977
 
13.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 58519
19.2%
P 46927
15.4%
y 46927
15.4%
N 35335
11.6%
o 35335
11.6%
w 35335
11.6%
t 11592
 
3.8%
e 11592
 
3.8%
L 11567
 
3.8%
r 11567
 
3.8%
Other values (6) 150
 
< 0.1%
Common
ValueCountFrequency (%)
46952
99.9%
- 25
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 351823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 58519
16.6%
46952
13.3%
P 46927
13.3%
y 46927
13.3%
N 35335
10.0%
o 35335
10.0%
w 35335
10.0%
t 11592
 
3.3%
e 11592
 
3.3%
L 11567
 
3.3%
Other values (8) 11742
 
3.3%

h_customer_id
Real number (ℝ)

Distinct24392
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5089212 × 1018
Minimum-9.096408 × 1018
Maximum9.2233353 × 1018
Zeros0
Zeros (%)0.0%
Negative445
Negative (%)0.9%
Memory size366.7 KiB
2023-06-07T23:00:44.697549image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum-9.096408 × 1018
5-th percentile4.0199151 × 1017
Q12.2306526 × 1018
median4.5488106 × 1018
Q36.8535869 × 1018
95-th percentile8.7526647 × 1018
Maximum9.2233353 × 1018
Range-1.2700075 × 1017
Interquartile range (IQR)4.6229344 × 1018

Descriptive statistics

Standard deviation2.8042853 × 1018
Coefficient of variation (CV)0.62194152
Kurtosis0.11193679
Mean4.5089212 × 1018
Median Absolute Deviation (MAD)2.311866 × 1018
Skewness-0.33204114
Sum5.9925552 × 1018
Variance7.8640161 × 1036
MonotonicityNot monotonic
2023-06-07T23:00:44.864789image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.476281626 × 1018258
 
0.5%
9.896276996 × 1017124
 
0.3%
3.403039646 × 1018113
 
0.2%
6.17110326 × 1018108
 
0.2%
2.260038697 × 101894
 
0.2%
2.84968879 × 101865
 
0.1%
6.096800854 × 101862
 
0.1%
7.537464804 × 101851
 
0.1%
6.357426652 × 101749
 
0.1%
7.190936377 × 101849
 
0.1%
Other values (24382) 45954
97.9%
ValueCountFrequency (%)
-9.096407971 × 10181
 
< 0.1%
-9.095945867 × 10181
 
< 0.1%
-9.077138756 × 10183
< 0.1%
-9.063637105 × 10181
 
< 0.1%
-9.029931 × 10182
 
< 0.1%
-9.020452036 × 10186
< 0.1%
-8.961035497 × 10181
 
< 0.1%
-8.931780579 × 10182
 
< 0.1%
-8.908509556 × 10181
 
< 0.1%
-8.896314417 × 10181
 
< 0.1%
ValueCountFrequency (%)
9.223335348 × 10181
 
< 0.1%
9.223298569 × 10184
< 0.1%
9.223219011 × 10181
 
< 0.1%
9.223204826 × 10181
 
< 0.1%
9.222879283 × 10181
 
< 0.1%
9.222675043 × 10182
 
< 0.1%
9.222136928 × 10181
 
< 0.1%
9.221635623 × 10181
 
< 0.1%
9.221401145 × 10188
< 0.1%
9.221262823 × 10181
 
< 0.1%
Distinct136
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:45.150031image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length32
Median length21
Mean length8.8196134
Min length4

Characters and Unicode

Total characters413878
Distinct characters55
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.1%

Sample

1st rowChina
2nd rowJapan
3rd rowTaiwan
4th rowTurkey
5th rowSouth Korea
ValueCountFrequency (%)
south 6364
 
10.0%
korea 6227
 
9.8%
malaysia 6068
 
9.5%
taiwan 5059
 
8.0%
thailand 3409
 
5.4%
united 3078
 
4.8%
china 2669
 
4.2%
japan 2330
 
3.7%
hong 2241
 
3.5%
kong 2241
 
3.5%
Other values (151) 23912
37.6%
2023-06-07T23:00:45.577792image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 68559
16.6%
i 37749
 
9.1%
n 33947
 
8.2%
o 24183
 
5.8%
e 22684
 
5.5%
t 16690
 
4.0%
16667
 
4.0%
r 14661
 
3.5%
s 14628
 
3.5%
h 14514
 
3.5%
Other values (45) 149596
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 322079
77.8%
Uppercase Letter 75123
 
18.2%
Space Separator 16671
 
4.0%
Dash Punctuation 3
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 68559
21.3%
i 37749
11.7%
n 33947
10.5%
o 24183
 
7.5%
e 22684
 
7.0%
t 16690
 
5.2%
r 14661
 
4.6%
s 14628
 
4.5%
h 14514
 
4.5%
l 14009
 
4.3%
Other values (16) 60455
18.8%
Uppercase Letter
ValueCountFrequency (%)
K 11677
15.5%
S 11036
14.7%
T 8579
11.4%
N 7190
9.6%
M 6499
8.7%
U 5347
7.1%
A 4143
 
5.5%
C 3152
 
4.2%
I 3066
 
4.1%
O 2363
 
3.1%
Other values (15) 12071
16.1%
Space Separator
ValueCountFrequency (%)
16667
> 99.9%
  4
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
' 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 397202
96.0%
Common 16676
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 68559
17.3%
i 37749
 
9.5%
n 33947
 
8.5%
o 24183
 
6.1%
e 22684
 
5.7%
t 16690
 
4.2%
r 14661
 
3.7%
s 14628
 
3.7%
h 14514
 
3.7%
l 14009
 
3.5%
Other values (41) 135578
34.1%
Common
ValueCountFrequency (%)
16667
99.9%
  4
 
< 0.1%
- 3
 
< 0.1%
' 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 413874
> 99.9%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 68559
16.6%
i 37749
 
9.1%
n 33947
 
8.2%
o 24183
 
5.8%
e 22684
 
5.5%
t 16690
 
4.0%
16667
 
4.0%
r 14661
 
3.5%
s 14628
 
3.5%
h 14514
 
3.5%
Other values (44) 149592
36.1%
None
ValueCountFrequency (%)
  4
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0
36872 
1
10055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46927
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Length

2023-06-07T23:00:45.729520image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:45.843779image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46927
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Common 46927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36872
78.6%
1 10055
 
21.4%
Distinct144
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:46.033451image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length32
Median length26
Mean length8.4160718
Min length4

Characters and Unicode

Total characters394941
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34 ?
Unique (%)0.1%

Sample

1st rowChina
2nd rowJapan
3rd rowTaiwan
4th rowTurkey
5th rowSouth Korea
ValueCountFrequency (%)
south 6765
 
11.2%
korea 6634
 
11.0%
malaysia 6163
 
10.2%
taiwan 5347
 
8.9%
thailand 3514
 
5.8%
united 3211
 
5.3%
china 3075
 
5.1%
japan 2469
 
4.1%
hong 2287
 
3.8%
kong 2287
 
3.8%
Other values (162) 18660
30.9%
2023-06-07T23:00:46.413647image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 69572
17.6%
i 37621
 
9.5%
n 35710
 
9.0%
o 23356
 
5.9%
e 21766
 
5.5%
t 17532
 
4.4%
h 15545
 
3.9%
s 15086
 
3.8%
l 14393
 
3.6%
13481
 
3.4%
Other values (47) 130879
33.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 321012
81.3%
Uppercase Letter 60439
 
15.3%
Space Separator 13485
 
3.4%
Other Punctuation 3
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 69572
21.7%
i 37621
11.7%
n 35710
11.1%
o 23356
 
7.3%
e 21766
 
6.8%
t 17532
 
5.5%
h 15545
 
4.8%
s 15086
 
4.7%
l 14393
 
4.5%
r 13380
 
4.2%
Other values (16) 57051
17.8%
Uppercase Letter
ValueCountFrequency (%)
S 11588
19.2%
K 9947
16.5%
T 8986
14.9%
M 6640
11.0%
C 3587
 
5.9%
U 3249
 
5.4%
I 3217
 
5.3%
J 2481
 
4.1%
H 2298
 
3.8%
A 2284
 
3.8%
Other values (15) 6162
10.2%
Space Separator
ValueCountFrequency (%)
13481
> 99.9%
  4
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
' 2
66.7%
& 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 381451
96.6%
Common 13490
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 69572
18.2%
i 37621
 
9.9%
n 35710
 
9.4%
o 23356
 
6.1%
e 21766
 
5.7%
t 17532
 
4.6%
h 15545
 
4.1%
s 15086
 
4.0%
l 14393
 
3.8%
r 13380
 
3.5%
Other values (41) 117490
30.8%
Common
ValueCountFrequency (%)
13481
99.9%
  4
 
< 0.1%
' 2
 
< 0.1%
& 1
 
< 0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 394937
> 99.9%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 69572
17.6%
i 37621
 
9.5%
n 35710
 
9.0%
o 23356
 
5.9%
e 21766
 
5.5%
t 17532
 
4.4%
h 15545
 
3.9%
s 15086
 
3.8%
l 14393
 
3.6%
13481
 
3.4%
Other values (46) 130875
33.1%
None
ValueCountFrequency (%)
  4
100.0%

no_of_adults
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3475611
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:46.558320image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q32
95-th percentile4
Maximum30
Range29
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3241378
Coefficient of variation (CV)0.56404829
Kurtosis33.488263
Mean2.3475611
Median Absolute Deviation (MAD)0
Skewness4.2137414
Sum110164
Variance1.753341
MonotonicityNot monotonic
2023-06-07T23:00:46.679534image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 32070
68.3%
1 5481
 
11.7%
4 4492
 
9.6%
3 2905
 
6.2%
6 926
 
2.0%
5 378
 
0.8%
8 301
 
0.6%
10 101
 
0.2%
7 81
 
0.2%
12 67
 
0.1%
Other values (11) 125
 
0.3%
ValueCountFrequency (%)
1 5481
 
11.7%
2 32070
68.3%
3 2905
 
6.2%
4 4492
 
9.6%
5 378
 
0.8%
6 926
 
2.0%
7 81
 
0.2%
8 301
 
0.6%
9 49
 
0.1%
10 101
 
0.2%
ValueCountFrequency (%)
30 1
 
< 0.1%
27 2
 
< 0.1%
20 1
 
< 0.1%
18 18
 
< 0.1%
17 2
 
< 0.1%
16 16
 
< 0.1%
15 4
 
< 0.1%
14 16
 
< 0.1%
13 3
 
< 0.1%
12 67
0.1%

no_of_children
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14814499
Minimum0
Maximum10
Zeros42690
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:46.807626image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52912642
Coefficient of variation (CV)3.5716795
Kurtosis25.197554
Mean0.14814499
Median Absolute Deviation (MAD)0
Skewness4.4201626
Sum6952
Variance0.27997477
MonotonicityNot monotonic
2023-06-07T23:00:46.917904image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 42690
91.0%
1 2117
 
4.5%
2 1726
 
3.7%
3 251
 
0.5%
4 109
 
0.2%
5 18
 
< 0.1%
6 12
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 42690
91.0%
1 2117
 
4.5%
2 1726
 
3.7%
3 251
 
0.5%
4 109
 
0.2%
5 18
 
< 0.1%
6 12
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 12
 
< 0.1%
5 18
 
< 0.1%
4 109
 
0.2%
3 251
 
0.5%
2 1726
 
3.7%
1 2117
 
4.5%
0 42690
91.0%

no_of_extra_bed
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
0
46416 
1
 
483
2
 
22
3
 
5
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters46927
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Length

2023-06-07T23:00:47.033260image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:47.159538image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46927
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 46927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 46416
98.9%
1 483
 
1.0%
2 22
 
< 0.1%
3 5
 
< 0.1%
5 1
 
< 0.1%

no_of_room
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.140303
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:47.262016image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.51904538
Coefficient of variation (CV)0.45518197
Kurtosis52.885359
Mean1.140303
Median Absolute Deviation (MAD)0
Skewness5.9691302
Sum53511
Variance0.26940811
MonotonicityNot monotonic
2023-06-07T23:00:47.366792image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 42320
90.2%
2 3426
 
7.3%
3 751
 
1.6%
4 244
 
0.5%
5 106
 
0.2%
6 32
 
0.1%
9 20
 
< 0.1%
7 16
 
< 0.1%
8 12
 
< 0.1%
ValueCountFrequency (%)
1 42320
90.2%
2 3426
 
7.3%
3 751
 
1.6%
4 244
 
0.5%
5 106
 
0.2%
6 32
 
0.1%
7 16
 
< 0.1%
8 12
 
< 0.1%
9 20
 
< 0.1%
ValueCountFrequency (%)
9 20
 
< 0.1%
8 12
 
< 0.1%
7 16
 
< 0.1%
6 32
 
0.1%
5 106
 
0.2%
4 244
 
0.5%
3 751
 
1.6%
2 3426
 
7.3%
1 42320
90.2%
Distinct141
Distinct (%)0.3%
Missing2
Missing (%)< 0.1%
Memory size366.7 KiB
2023-06-07T23:00:47.605140image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters93850
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st rowCN
2nd rowJP
3rd rowTW
4th rowTR
5th rowKR
ValueCountFrequency (%)
kr 6298
13.4%
my 6185
13.2%
tw 5255
11.2%
th 4308
 
9.2%
jp 2530
 
5.4%
cn 2494
 
5.3%
hk 2424
 
5.2%
id 2263
 
4.8%
us 1960
 
4.2%
sg 1926
 
4.1%
Other values (131) 11282
24.0%
2023-06-07T23:00:47.966665image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 10075
 
10.7%
K 9274
 
9.9%
H 8972
 
9.6%
R 7083
 
7.5%
M 6936
 
7.4%
Y 6207
 
6.6%
W 5316
 
5.7%
N 5056
 
5.4%
S 4588
 
4.9%
P 4583
 
4.9%
Other values (17) 25760
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 93784
99.9%
Decimal Number 66
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 10075
 
10.7%
K 9274
 
9.9%
H 8972
 
9.6%
R 7083
 
7.6%
M 6936
 
7.4%
Y 6207
 
6.6%
W 5316
 
5.7%
N 5056
 
5.4%
S 4588
 
4.9%
P 4583
 
4.9%
Other values (16) 25694
27.4%
Decimal Number
ValueCountFrequency (%)
1 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 93784
99.9%
Common 66
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 10075
 
10.7%
K 9274
 
9.9%
H 8972
 
9.6%
R 7083
 
7.6%
M 6936
 
7.4%
Y 6207
 
6.6%
W 5316
 
5.7%
N 5056
 
5.4%
S 4588
 
4.9%
P 4583
 
4.9%
Other values (16) 25694
27.4%
Common
ValueCountFrequency (%)
1 66
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 10075
 
10.7%
K 9274
 
9.9%
H 8972
 
9.6%
R 7083
 
7.5%
M 6936
 
7.4%
Y 6207
 
6.6%
W 5316
 
5.7%
N 5056
 
5.4%
S 4588
 
4.9%
P 4583
 
4.9%
Other values (17) 25760
27.4%

language
Categorical

Distinct49
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
English
17238 
Korean
6684 
T. Chinese / Taiwan
5776 
S.Chinese / Mainland
3217 
Japanese
2436 
Other values (44)
11576 

Length

Max length24
Median length22
Mean length10.479511
Min length4

Characters and Unicode

Total characters491772
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowS.Chinese / Mainland
2nd rowJapanese
3rd rowT. Chinese / Taiwan
4th rowTurkish
5th rowKorean

Common Values

ValueCountFrequency (%)
English 17238
36.7%
Korean 6684
 
14.2%
T. Chinese / Taiwan 5776
 
12.3%
S.Chinese / Mainland 3217
 
6.9%
Japanese 2436
 
5.2%
Thai 2399
 
5.1%
T.Chinese / Hongkong 1929
 
4.1%
English / United Kingdom 1293
 
2.8%
Indonesian 765
 
1.6%
Vietnamese 636
 
1.4%
Other values (39) 4554
 
9.7%

Length

2023-06-07T23:00:48.131968image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english 19789
24.4%
13516
16.7%
korean 6684
 
8.2%
t 5776
 
7.1%
chinese 5776
 
7.1%
taiwan 5776
 
7.1%
s.chinese 3217
 
4.0%
mainland 3217
 
4.0%
japanese 2436
 
3.0%
thai 2399
 
3.0%
Other values (46) 12465
15.4%

Most occurring characters

ValueCountFrequency (%)
n 64199
13.1%
i 48835
 
9.9%
e 39552
 
8.0%
a 38076
 
7.7%
s 36138
 
7.3%
h 34355
 
7.0%
34124
 
6.9%
g 25524
 
5.2%
l 24076
 
4.9%
E 19790
 
4.0%
Other values (39) 127103
25.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 360455
73.3%
Uppercase Letter 72718
 
14.8%
Space Separator 34124
 
6.9%
Other Punctuation 24438
 
5.0%
Dash Punctuation 37
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 64199
17.8%
i 48835
13.5%
e 39552
11.0%
a 38076
10.6%
s 36138
10.0%
h 34355
9.5%
g 25524
 
7.1%
l 24076
 
6.7%
o 13257
 
3.7%
r 9563
 
2.7%
Other values (14) 26880
7.5%
Uppercase Letter
ValueCountFrequency (%)
E 19790
27.2%
T 15945
21.9%
C 11081
15.2%
K 7977
11.0%
S 3992
 
5.5%
M 3495
 
4.8%
J 2436
 
3.3%
H 2093
 
2.9%
U 1297
 
1.8%
I 1140
 
1.6%
Other values (11) 3472
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/ 13516
55.3%
. 10922
44.7%
Space Separator
ValueCountFrequency (%)
34124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 433173
88.1%
Common 58599
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 64199
14.8%
i 48835
11.3%
e 39552
9.1%
a 38076
8.8%
s 36138
8.3%
h 34355
 
7.9%
g 25524
 
5.9%
l 24076
 
5.6%
E 19790
 
4.6%
T 15945
 
3.7%
Other values (35) 86683
20.0%
Common
ValueCountFrequency (%)
34124
58.2%
/ 13516
 
23.1%
. 10922
 
18.6%
- 37
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 491772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 64199
13.1%
i 48835
 
9.9%
e 39552
 
8.0%
a 38076
 
7.7%
s 36138
 
7.3%
h 34355
 
7.0%
34124
 
6.9%
g 25524
 
5.2%
l 24076
 
4.9%
E 19790
 
4.0%
Other values (39) 127103
25.8%

original_selling_amount
Real number (ℝ)

Distinct26481
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.59182
Minimum2.1
Maximum49566.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:48.299546image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile17.7
Q150.585
median107.78
Q3243.01
95-th percentile750.268
Maximum49566.16
Range49564.06
Interquartile range (IQR)192.425

Descriptive statistics

Standard deviation439.9447
Coefficient of variation (CV)2.0034658
Kurtosis3515.9943
Mean219.59182
Median Absolute Deviation (MAD)72.27
Skewness36.550096
Sum10304785
Variance193551.34
MonotonicityNot monotonic
2023-06-07T23:00:48.461446image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122.63 14
 
< 0.1%
15 12
 
< 0.1%
37.05 12
 
< 0.1%
22 11
 
< 0.1%
45 11
 
< 0.1%
19.56 11
 
< 0.1%
222.94 10
 
< 0.1%
28.46 10
 
< 0.1%
30 10
 
< 0.1%
16.86 10
 
< 0.1%
Other values (26471) 46816
99.8%
ValueCountFrequency (%)
2.1 1
< 0.1%
2.27 1
< 0.1%
2.31 1
< 0.1%
2.5 1
< 0.1%
2.59 1
< 0.1%
2.64 1
< 0.1%
2.76 1
< 0.1%
2.8 1
< 0.1%
2.82 1
< 0.1%
2.9 1
< 0.1%
ValueCountFrequency (%)
49566.16 1
< 0.1%
17942.82 1
< 0.1%
15430.5 1
< 0.1%
13015.52 1
< 0.1%
11672.15 1
< 0.1%
9562.86 1
< 0.1%
9074.52 1
< 0.1%
9065.76 1
< 0.1%
8024.56 1
< 0.1%
7437.78 1
< 0.1%
Distinct36
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Visa
23088 
MasterCard
13875 
UNKNOWN
3210 
American Express
 
2198
JCB
 
1124
Other values (31)
3432 

Length

Max length21
Median length20
Mean length6.8831163
Min length3

Characters and Unicode

Total characters323004
Distinct characters49
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAlipay
2nd rowAmerican Express
3rd rowVisa
4th rowMasterCard
5th rowVisa

Common Values

ValueCountFrequency (%)
Visa 23088
49.2%
MasterCard 13875
29.6%
UNKNOWN 3210
 
6.8%
American Express 2198
 
4.7%
JCB 1124
 
2.4%
Alipay 945
 
2.0%
PayPal 557
 
1.2%
MayBank2U 430
 
0.9%
UnionPay - Creditcard 292
 
0.6%
K PLUS 206
 
0.4%
Other values (26) 1002
 
2.1%

Length

2023-06-07T23:00:48.615316image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
visa 23088
45.5%
mastercard 13875
27.3%
unknown 3210
 
6.3%
american 2198
 
4.3%
express 2198
 
4.3%
jcb 1124
 
2.2%
alipay 945
 
1.9%
paypal 557
 
1.1%
maybank2u 430
 
0.8%
unionpay 339
 
0.7%
Other values (43) 2784
 
5.5%

Most occurring characters

ValueCountFrequency (%)
a 57861
17.9%
s 41874
13.0%
r 32986
10.2%
i 27345
8.5%
V 23088
 
7.1%
e 19220
 
6.0%
C 15988
 
4.9%
M 14788
 
4.6%
t 14581
 
4.5%
d 14574
 
4.5%
Other values (39) 60699
18.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 228931
70.9%
Uppercase Letter 89464
 
27.7%
Space Separator 3821
 
1.2%
Decimal Number 449
 
0.1%
Dash Punctuation 339
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 57861
25.3%
s 41874
18.3%
r 32986
14.4%
i 27345
11.9%
e 19220
 
8.4%
t 14581
 
6.4%
d 14574
 
6.4%
n 3570
 
1.6%
p 3303
 
1.4%
c 2737
 
1.2%
Other values (13) 10880
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
V 23088
25.8%
C 15988
17.9%
M 14788
16.5%
N 9652
10.8%
U 4185
 
4.7%
K 3449
 
3.9%
W 3411
 
3.8%
A 3382
 
3.8%
O 3222
 
3.6%
E 2248
 
2.5%
Other values (12) 6051
 
6.8%
Decimal Number
ValueCountFrequency (%)
2 430
95.8%
7 19
 
4.2%
Space Separator
ValueCountFrequency (%)
3821
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 339
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 318395
98.6%
Common 4609
 
1.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 57861
18.2%
s 41874
13.2%
r 32986
10.4%
i 27345
8.6%
V 23088
 
7.3%
e 19220
 
6.0%
C 15988
 
5.0%
M 14788
 
4.6%
t 14581
 
4.6%
d 14574
 
4.6%
Other values (35) 56090
17.6%
Common
ValueCountFrequency (%)
3821
82.9%
2 430
 
9.3%
- 339
 
7.4%
7 19
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 323004
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 57861
17.9%
s 41874
13.0%
r 32986
10.2%
i 27345
8.5%
V 23088
 
7.1%
e 19220
 
6.0%
C 15988
 
4.9%
M 14788
 
4.6%
t 14581
 
4.5%
d 14574
 
4.5%
Other values (39) 60699
18.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
Credit Card
46132 
Invoice
 
612
Gift Card
 
183

Length

Max length11
Median length11
Mean length10.940035
Min length7

Characters and Unicode

Total characters513383
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCredit Card
2nd rowCredit Card
3rd rowCredit Card
4th rowCredit Card
5th rowCredit Card

Common Values

ValueCountFrequency (%)
Credit Card 46132
98.3%
Invoice 612
 
1.3%
Gift Card 183
 
0.4%

Length

2023-06-07T23:00:48.758639image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:48.895734image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
card 46315
49.7%
credit 46132
49.5%
invoice 612
 
0.7%
gift 183
 
0.2%

Most occurring characters

ValueCountFrequency (%)
C 92447
18.0%
r 92447
18.0%
d 92447
18.0%
i 46927
9.1%
e 46744
9.1%
t 46315
9.0%
46315
9.0%
a 46315
9.0%
I 612
 
0.1%
n 612
 
0.1%
Other values (5) 2202
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 373826
72.8%
Uppercase Letter 93242
 
18.2%
Space Separator 46315
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 92447
24.7%
d 92447
24.7%
i 46927
12.6%
e 46744
12.5%
t 46315
12.4%
a 46315
12.4%
n 612
 
0.2%
v 612
 
0.2%
o 612
 
0.2%
c 612
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 92447
99.1%
I 612
 
0.7%
G 183
 
0.2%
Space Separator
ValueCountFrequency (%)
46315
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 467068
91.0%
Common 46315
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 92447
19.8%
r 92447
19.8%
d 92447
19.8%
i 46927
10.0%
e 46744
10.0%
t 46315
9.9%
a 46315
9.9%
I 612
 
0.1%
n 612
 
0.1%
v 612
 
0.1%
Other values (4) 1590
 
0.3%
Common
ValueCountFrequency (%)
46315
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 513383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 92447
18.0%
r 92447
18.0%
d 92447
18.0%
i 46927
9.1%
e 46744
9.1%
t 46315
9.0%
46315
9.0%
a 46315
9.0%
I 612
 
0.1%
n 612
 
0.1%
Other values (5) 2202
 
0.4%
Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
MYR
6088 
KRW
5995 
TWD
5300 
USD
3685 
THB
3604 
Other values (45)
22255 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters140781
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNY
2nd rowJPY
3rd rowTWD
4th rowTRY
5th rowKRW

Common Values

ValueCountFrequency (%)
MYR 6088
13.0%
KRW 5995
12.8%
TWD 5300
11.3%
USD 3685
 
7.9%
THB 3604
 
7.7%
CNY 2908
 
6.2%
HKD 2695
 
5.7%
JPY 2677
 
5.7%
SGD 1977
 
4.2%
IDR 1908
 
4.1%
Other values (40) 10090
21.5%

Length

2023-06-07T23:00:48.997755image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
myr 6088
13.0%
krw 5995
12.8%
twd 5300
11.3%
usd 3685
 
7.9%
thb 3604
 
7.7%
cny 2908
 
6.2%
hkd 2695
 
5.7%
jpy 2677
 
5.7%
sgd 1977
 
4.2%
idr 1908
 
4.1%
Other values (40) 10090
21.5%

Most occurring characters

ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 140781
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 140781
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140781
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 18718
13.3%
R 17659
12.5%
Y 11789
 
8.4%
W 11352
 
8.1%
K 9238
 
6.6%
T 9034
 
6.4%
H 8063
 
5.7%
U 7111
 
5.1%
P 6972
 
5.0%
S 6444
 
4.6%
Other values (16) 34401
24.4%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
True
30549 
False
16378 
ValueCountFrequency (%)
True 30549
65.1%
False 16378
34.9%
2023-06-07T23:00:49.114331image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Distinct719
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:49.492875image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Length

Max length20
Median length19
Mean length9.8295864
Min length4

Characters and Unicode

Total characters461273
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique348 ?
Unique (%)0.7%

Sample

1st row1D1N_1N
2nd row3D1N_1N
3rd row1D1N_1N
4th row365D100P_100P
5th row3D100P_100P
ValueCountFrequency (%)
365d100p_100p 12793
27.3%
1d1n_1n 7026
15.0%
3d1n_1n 2712
 
5.8%
1d100p 2008
 
4.3%
3d1n_100p 1984
 
4.2%
1d100p_100p 1361
 
2.9%
7d100p_100p 1258
 
2.7%
3d100p_100p 1220
 
2.6%
2d100p 1175
 
2.5%
3d100p 1125
 
2.4%
Other values (709) 14265
30.4%
2023-06-07T23:00:49.927813image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 116602
25.3%
1 98713
21.4%
P 58967
12.8%
D 48674
10.6%
_ 41142
 
8.9%
N 30034
 
6.5%
3 22793
 
4.9%
5 16343
 
3.5%
6 13603
 
2.9%
2 4778
 
1.0%
Other values (8) 9624
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 280592
60.8%
Uppercase Letter 139539
30.3%
Connector Punctuation 41142
 
8.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 116602
41.6%
1 98713
35.2%
3 22793
 
8.1%
5 16343
 
5.8%
6 13603
 
4.8%
2 4778
 
1.7%
7 4537
 
1.6%
4 2485
 
0.9%
8 454
 
0.2%
9 284
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 58967
42.3%
D 48674
34.9%
N 30034
21.5%
U 466
 
0.3%
K 466
 
0.3%
O 466
 
0.3%
W 466
 
0.3%
Connector Punctuation
ValueCountFrequency (%)
_ 41142
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 321734
69.7%
Latin 139539
30.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 116602
36.2%
1 98713
30.7%
_ 41142
 
12.8%
3 22793
 
7.1%
5 16343
 
5.1%
6 13603
 
4.2%
2 4778
 
1.5%
7 4537
 
1.4%
4 2485
 
0.8%
8 454
 
0.1%
Latin
ValueCountFrequency (%)
P 58967
42.3%
D 48674
34.9%
N 30034
21.5%
U 466
 
0.3%
K 466
 
0.3%
O 466
 
0.3%
W 466
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 461273
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 116602
25.3%
1 98713
21.4%
P 58967
12.8%
D 48674
10.6%
_ 41142
 
8.9%
N 30034
 
6.5%
3 22793
 
4.9%
5 16343
 
3.5%
6 13603
 
2.9%
2 4778
 
1.0%
Other values (8) 9624
 
2.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size46.0 KiB
False
36484 
True
10443 
ValueCountFrequency (%)
False 36484
77.7%
True 10443
 
22.3%
2023-06-07T23:00:50.081612image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
1.0
19328 
0.0
7569 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 19328
41.2%
0.0 7569
 
16.1%
(Missing) 20030
42.7%

Length

2023-06-07T23:00:50.181690image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:50.305964image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 19328
71.9%
0.0 7569
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 34466
42.7%
. 26897
33.3%
1 19328
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34466
64.1%
1 19328
35.9%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34466
42.7%
. 26897
33.3%
1 19328
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34466
42.7%
. 26897
33.3%
1 19328
24.0%

request_latecheckin
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
26195 
1.0
 
702

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 26195
55.8%
1.0 702
 
1.5%
(Missing) 20030
42.7%

Length

2023-06-07T23:00:50.407633image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:50.527018image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 26195
97.4%
1.0 702
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 53092
65.8%
. 26897
33.3%
1 702
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53092
98.7%
1 702
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53092
65.8%
. 26897
33.3%
1 702
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53092
65.8%
. 26897
33.3%
1 702
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
22844 
1.0
4053 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22844
48.7%
1.0 4053
 
8.6%
(Missing) 20030
42.7%

Length

2023-06-07T23:00:50.629496image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:50.749076image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22844
84.9%
1.0 4053
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 49741
61.6%
. 26897
33.3%
1 4053
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49741
92.5%
1 4053
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49741
61.6%
. 26897
33.3%
1 4053
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49741
61.6%
. 26897
33.3%
1 4053
 
5.0%

request_largebed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
16391 
1.0
10506 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 16391
34.9%
1.0 10506
22.4%
(Missing) 20030
42.7%

Length

2023-06-07T23:00:50.868154image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:51.005574image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16391
60.9%
1.0 10506
39.1%

Most occurring characters

ValueCountFrequency (%)
0 43288
53.6%
. 26897
33.3%
1 10506
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43288
80.5%
1 10506
 
19.5%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43288
53.6%
. 26897
33.3%
1 10506
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43288
53.6%
. 26897
33.3%
1 10506
 
13.0%

request_twinbeds
Categorical

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
22624 
1.0
4273 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 22624
48.2%
1.0 4273
 
9.1%
(Missing) 20030
42.7%

Length

2023-06-07T23:00:51.107746image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:51.230292image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 22624
84.1%
1.0 4273
 
15.9%

Most occurring characters

ValueCountFrequency (%)
0 49521
61.4%
. 26897
33.3%
1 4273
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49521
92.1%
1 4273
 
7.9%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49521
61.4%
. 26897
33.3%
1 4273
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49521
61.4%
. 26897
33.3%
1 4273
 
5.3%

request_airport
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
26698 
1.0
 
199

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 26698
56.9%
1.0 199
 
0.4%
(Missing) 20030
42.7%

Length

2023-06-07T23:00:51.333158image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:51.457828image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 26698
99.3%
1.0 199
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 53595
66.4%
. 26897
33.3%
1 199
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53595
99.6%
1 199
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53595
66.4%
. 26897
33.3%
1 199
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53595
66.4%
. 26897
33.3%
1 199
 
0.2%

request_earlycheckin
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing20030
Missing (%)42.7%
Memory size366.7 KiB
0.0
25996 
1.0
 
901

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80691
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 25996
55.4%
1.0 901
 
1.9%
(Missing) 20030
42.7%

Length

2023-06-07T23:00:51.556152image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-07T23:00:51.677664image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 25996
96.7%
1.0 901
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 52893
65.6%
. 26897
33.3%
1 901
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53794
66.7%
Other Punctuation 26897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 52893
98.3%
1 901
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 26897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 52893
65.6%
. 26897
33.3%
1 901
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 52893
65.6%
. 26897
33.3%
1 901
 
1.1%

hotel_area_code
Real number (ℝ)

Distinct5057
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3025.0395
Minimum1
Maximum5896
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:51.798685image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile243
Q11477
median3134
Q34572
95-th percentile5602
Maximum5896
Range5895
Interquartile range (IQR)3095

Descriptive statistics

Standard deviation1733.8559
Coefficient of variation (CV)0.573168
Kurtosis-1.2487545
Mean3025.0395
Median Absolute Deviation (MAD)1549
Skewness-0.11513251
Sum1.4195603 × 108
Variance3006256.2
MonotonicityNot monotonic
2023-06-07T23:00:51.952182image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3747 718
 
1.5%
1192 571
 
1.2%
643 419
 
0.9%
4463 404
 
0.9%
606 382
 
0.8%
104 372
 
0.8%
4364 342
 
0.7%
3156 335
 
0.7%
2553 322
 
0.7%
5891 302
 
0.6%
Other values (5047) 42760
91.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 7
< 0.1%
5 1
 
< 0.1%
6 5
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 7
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
5896 1
 
< 0.1%
5894 2
 
< 0.1%
5893 1
 
< 0.1%
5892 2
 
< 0.1%
5891 302
0.6%
5890 1
 
< 0.1%
5889 18
 
< 0.1%
5888 1
 
< 0.1%
5887 39
 
0.1%
5886 10
 
< 0.1%

hotel_brand_code
Real number (ℝ)

Distinct850
Distinct (%)7.0%
Missing34699
Missing (%)73.9%
Infinite0
Infinite (%)0.0%
Mean479.07458
Minimum0
Maximum936
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:52.115660image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1253
median481
Q3741
95-th percentile914
Maximum936
Range936
Interquartile range (IQR)488

Descriptive statistics

Standard deviation277.61635
Coefficient of variation (CV)0.57948461
Kurtosis-1.1793446
Mean479.07458
Median Absolute Deviation (MAD)243
Skewness-0.0051064914
Sum5858124
Variance77070.837
MonotonicityNot monotonic
2023-06-07T23:00:52.272568image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
777 293
 
0.6%
442 254
 
0.5%
51 222
 
0.5%
253 215
 
0.5%
593 205
 
0.4%
918 195
 
0.4%
520 176
 
0.4%
50 160
 
0.3%
789 157
 
0.3%
193 155
 
0.3%
Other values (840) 10196
 
21.7%
(Missing) 34699
73.9%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 3
 
< 0.1%
2 3
 
< 0.1%
3 35
0.1%
4 2
 
< 0.1%
6 13
 
< 0.1%
7 5
 
< 0.1%
8 3
 
< 0.1%
9 65
0.1%
10 28
0.1%
ValueCountFrequency (%)
936 66
0.1%
935 11
 
< 0.1%
934 7
 
< 0.1%
933 58
0.1%
932 7
 
< 0.1%
931 28
0.1%
930 2
 
< 0.1%
929 1
 
< 0.1%
928 4
 
< 0.1%
927 2
 
< 0.1%

hotel_chain_code
Real number (ℝ)

Distinct610
Distinct (%)4.8%
Missing34343
Missing (%)73.2%
Infinite0
Infinite (%)0.0%
Mean357.64781
Minimum0
Maximum680
Zeros8
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:52.431797image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28
Q1181
median355
Q3575
95-th percentile672
Maximum680
Range680
Interquartile range (IQR)394

Descriptive statistics

Standard deviation208.81905
Coefficient of variation (CV)0.58386783
Kurtosis-1.3361399
Mean357.64781
Median Absolute Deviation (MAD)209
Skewness-0.058302719
Sum4500640
Variance43605.395
MonotonicityNot monotonic
2023-06-07T23:00:52.589217image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 696
 
1.5%
296 607
 
1.3%
675 383
 
0.8%
55 363
 
0.8%
181 340
 
0.7%
386 279
 
0.6%
537 249
 
0.5%
587 232
 
0.5%
217 222
 
0.5%
583 214
 
0.5%
Other values (600) 8999
 
19.2%
(Missing) 34343
73.2%
ValueCountFrequency (%)
0 8
 
< 0.1%
1 50
0.1%
2 39
0.1%
3 1
 
< 0.1%
4 2
 
< 0.1%
5 2
 
< 0.1%
7 3
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
10 50
0.1%
ValueCountFrequency (%)
680 3
 
< 0.1%
679 52
 
0.1%
678 117
 
0.2%
677 13
 
< 0.1%
676 2
 
< 0.1%
675 383
0.8%
674 15
 
< 0.1%
673 27
 
0.1%
672 26
 
0.1%
671 2
 
< 0.1%

hotel_city_code
Real number (ℝ)

Distinct2402
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1498.0466
Minimum0
Maximum2808
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size366.7 KiB
2023-06-07T23:00:52.755729image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile140
Q1583
median1572
Q32310
95-th percentile2797
Maximum2808
Range2808
Interquartile range (IQR)1727

Descriptive statistics

Standard deviation909.02087
Coefficient of variation (CV)0.60680414
Kurtosis-1.4051834
Mean1498.0466
Median Absolute Deviation (MAD)875
Skewness-0.16917617
Sum70298832
Variance826318.94
MonotonicityNot monotonic
2023-06-07T23:00:52.905548image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2477 2525
 
5.4%
2797 1883
 
4.0%
1403 1794
 
3.8%
142 1295
 
2.8%
162 1163
 
2.5%
2249 1124
 
2.4%
437 1059
 
2.3%
2799 987
 
2.1%
1816 881
 
1.9%
2310 767
 
1.6%
Other values (2392) 33449
71.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 2
 
< 0.1%
3 10
 
< 0.1%
4 2
 
< 0.1%
5 20
< 0.1%
6 3
 
< 0.1%
8 29
0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
2808 2
 
< 0.1%
2807 3
 
< 0.1%
2806 1
 
< 0.1%
2805 1
 
< 0.1%
2804 2
 
< 0.1%
2803 1
 
< 0.1%
2802 1
 
< 0.1%
2800 10
 
< 0.1%
2799 987
2.1%
2797 1883
4.0%
Distinct347
Distinct (%)2.7%
Missing34250
Missing (%)73.0%
Memory size366.7 KiB
Minimum2017-08-12 00:00:00
Maximum2019-03-09 00:00:00
2023-06-07T23:00:53.083766image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:53.267272image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-06-07T23:00:37.417867image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:11.664123image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:13.936234image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:17.145074image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:19.912255image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:22.387567image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:25.169013image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:27.593481image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:30.173296image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:31.989935image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:33.869979image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:35.505772image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:37.576440image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:11.860830image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:14.138691image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:17.431318image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:20.225403image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:22.574992image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:25.474197image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:28.098624image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:30.357222image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:32.157055image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:34.016778image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:35.660014image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:37.724046image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:12.042345image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:14.316217image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:17.746044image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:20.469749image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:22.763449image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:25.702586image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:28.300081image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:30.513210image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:32.309418image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:34.149057image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:35.967027image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:37.866747image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:12.224860image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:14.524658image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:18.014330image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:20.662494image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:22.944963image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:25.936960image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:28.478601image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:30.657489image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:32.457272image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:34.274723image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:36.102611image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:38.030311image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:12.445272image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:14.724125image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:18.240721image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:20.860789image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:23.194296image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:26.198259image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:28.673573image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:30.813470image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:32.626816image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:34.424489image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:36.250871image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:38.173290image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:12.620799image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:14.920605image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:18.429219image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:21.028382image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:23.468562image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:26.352528image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:28.854092image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:30.962020image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:32.808644image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:34.558588image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:36.403903image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:38.310581image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:12.794336image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:15.081169image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:18.632673image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:21.227805image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:23.776738image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:26.491206image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:29.004687image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:31.097494image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:32.958905image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:34.695367image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:36.548563image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:38.465904image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:12.979925image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:15.256802image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:18.842120image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:21.449214image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:23.972214image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:26.650065image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:29.186687image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:31.253903image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:33.120155image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:34.839077image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:36.698114image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:38.606255image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:13.166293image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:15.623959image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:19.097435image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:21.659869image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:24.213570image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:26.899860image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:29.477910image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:31.400249image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:33.281232image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:34.986139image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:36.851218image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:38.753995image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:13.368750image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:16.240310image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:19.312850image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:21.856347image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:24.476864image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:27.092819image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:29.643793image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:31.538844image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:33.435781image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:35.116399image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:36.995478image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:38.881919image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:13.524336image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:16.612674image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:19.492367image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:22.024893image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:24.640428image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:27.245411image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:29.800335image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:31.672553image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:33.579240image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:35.236746image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:37.126090image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:39.027717image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:13.715867image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:16.936630image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:19.740700image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:22.206408image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:24.882779image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:27.413798image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:29.987792image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:31.835713image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:33.727848image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:35.375174image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
2023-06-07T23:00:37.267745image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/

Correlations

2023-06-07T23:00:53.446183image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
h_booking_idhotel_idhotel_star_ratingh_customer_idno_of_adultsno_of_childrenno_of_roomoriginal_selling_amounthotel_area_codehotel_brand_codehotel_chain_codehotel_city_codeaccommadation_type_namecharge_optionguest_is_not_the_customerno_of_extra_bedlanguageoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_inis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckin
h_booking_id1.000-0.0030.0040.0040.0030.0010.0070.006-0.000-0.013-0.0100.0020.0000.0000.0080.0030.0000.0040.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.000
hotel_id-0.0031.000-0.171-0.010-0.022-0.048-0.031-0.1100.006-0.0360.0530.0260.0870.1150.0410.0280.0560.0660.0620.0670.0580.0220.0350.0190.0360.0250.0240.0000.017
hotel_star_rating0.004-0.1711.0000.0170.0940.0780.0360.445-0.002-0.017-0.097-0.0240.1950.0680.0480.0210.0810.1000.0550.1080.0290.0380.0390.0210.1620.0970.0780.0320.050
h_customer_id0.004-0.0100.0171.0000.0180.0100.0060.015-0.0020.009-0.025-0.0090.0270.0200.0230.0000.0370.0300.0630.0570.0320.0150.0170.0430.0070.0290.0240.0000.000
no_of_adults0.003-0.0220.0940.0181.0000.0470.5290.2580.012-0.025-0.0040.0130.0570.0420.0320.1060.0290.0000.0190.0360.0180.0080.0290.0000.0070.0990.0800.0330.000
no_of_children0.001-0.0480.0780.0100.0471.0000.0640.161-0.018-0.036-0.0140.0170.0230.0640.0200.0370.0300.0250.0070.0440.0060.0380.0000.0110.0100.0670.0250.0200.031
no_of_room0.007-0.0310.0360.0060.5290.0641.0000.219-0.0050.005-0.001-0.0110.0190.0340.0440.1020.0250.0080.0100.0350.0200.0080.0280.0000.0040.0520.0680.0270.000
original_selling_amount0.006-0.1100.4450.0150.2580.1610.2191.000-0.032-0.010-0.053-0.0420.0000.0060.0130.0000.0300.0000.0000.0340.0000.0000.0040.0000.0000.0000.0000.0220.000
hotel_area_code-0.0000.006-0.002-0.0020.012-0.018-0.005-0.0321.0000.0440.0200.0130.0520.0300.0500.0110.0600.0370.1000.0780.0350.0240.0330.0240.0410.0110.0190.0280.000
hotel_brand_code-0.013-0.036-0.0170.009-0.025-0.0360.005-0.0100.0441.000-0.050-0.0110.1100.0660.0330.0140.0770.0580.0210.0990.0310.0430.0360.0230.0400.0120.0160.0120.025
hotel_chain_code-0.0100.053-0.097-0.025-0.004-0.014-0.001-0.0530.020-0.0501.000-0.0130.0980.0870.0410.0170.0930.0620.0180.1170.0780.0810.0000.0290.0330.0350.0220.0160.056
hotel_city_code0.0020.026-0.024-0.0090.0130.017-0.011-0.0420.013-0.011-0.0131.0000.0930.0520.0610.0150.1280.0550.0950.1720.0400.0530.0320.0280.0480.0290.0020.0240.038
accommadation_type_name0.0000.0870.1950.0270.0570.0230.0190.0000.0520.1100.0980.0931.0000.0440.0540.0280.0550.0420.0280.0620.0250.0420.0600.0000.1110.1050.0740.0310.031
charge_option0.0000.1150.0680.0200.0420.0640.0340.0060.0300.0660.0870.0520.0441.0000.0440.0240.1550.1660.0480.1730.0960.0380.0220.0230.0570.0530.0330.0440.000
guest_is_not_the_customer0.0080.0410.0480.0230.0320.0200.0440.0130.0500.0330.0410.0610.0540.0441.0000.0140.1610.0790.0820.1990.0460.1620.0140.0190.0210.0000.0420.0090.028
no_of_extra_bed0.0030.0280.0210.0000.1060.0370.1020.0000.0110.0140.0170.0150.0280.0240.0141.0000.0310.0000.0000.0470.0000.0130.0140.0170.0000.0310.0310.0440.013
language0.0000.0560.0810.0370.0290.0300.0250.0300.0600.0770.0930.1280.0550.1550.1610.0311.0000.1610.2990.5350.2030.1920.0790.0890.1470.1280.0990.0610.102
original_payment_method0.0040.0660.1000.0300.0000.0250.0080.0000.0370.0580.0620.0550.0420.1660.0790.0000.1611.0000.3410.1630.1230.0960.0480.0330.0930.0940.0550.0000.054
original_payment_type0.0000.0620.0550.0630.0190.0070.0100.0000.1000.0210.0180.0950.0280.0480.0820.0000.2990.3411.0000.3160.1630.0530.0190.0000.0150.0170.0000.0000.000
original_payment_currency0.0090.0670.1080.0570.0360.0440.0350.0340.0780.0990.1170.1720.0620.1730.1990.0470.5350.1630.3161.0000.1990.2180.0740.0810.1920.1500.1040.1250.105
is_user_logged_in0.0000.0580.0290.0320.0180.0060.0200.0000.0350.0310.0780.0400.0250.0960.0460.0000.2030.1230.1630.1991.0000.4790.0190.0500.1190.0220.0000.0000.056
is_first_booking0.0000.0220.0380.0150.0080.0380.0080.0000.0240.0430.0810.0530.0420.0380.1620.0130.1920.0960.0530.2180.4791.0000.0320.0430.0860.0000.0000.0250.046
request_nonesmoke0.0000.0350.0390.0170.0290.0000.0280.0040.0330.0360.0000.0320.0600.0220.0140.0140.0790.0480.0190.0740.0190.0321.0000.0430.0380.0910.0660.0140.038
request_latecheckin0.0000.0190.0210.0430.0000.0110.0000.0000.0240.0230.0290.0280.0000.0230.0190.0170.0890.0330.0000.0810.0500.0430.0431.0000.1120.0390.0040.0110.029
request_highfloor0.0000.0360.1620.0070.0070.0100.0040.0000.0410.0400.0330.0480.1110.0570.0210.0000.1470.0930.0150.1920.1190.0860.0380.1121.0000.1520.0450.0300.171
request_largebed0.0000.0250.0970.0290.0990.0670.0520.0000.0110.0120.0350.0290.1050.0530.0000.0310.1280.0940.0170.1500.0220.0000.0910.0390.1521.0000.3480.0160.038
request_twinbeds0.0000.0240.0780.0240.0800.0250.0680.0000.0190.0160.0220.0020.0740.0330.0420.0310.0990.0550.0000.1040.0000.0000.0660.0040.0450.3481.0000.0000.002
request_airport0.0000.0000.0320.0000.0330.0200.0270.0220.0280.0120.0160.0240.0310.0440.0090.0440.0610.0000.0000.1250.0000.0250.0140.0110.0300.0160.0001.0000.014
request_earlycheckin0.0000.0170.0500.0000.0000.0310.0000.0000.0000.0250.0560.0380.0310.0000.0280.0130.1020.0540.0000.1050.0560.0460.0380.0290.1710.0380.0020.0141.000

Missing values

2023-06-07T23:00:39.335104image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-07T23:00:40.125202image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-07T23:00:40.839208image/svg+xmlMatplotlib v3.6.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

h_booking_idbooking_datetimecheckin_datecheckout_datehotel_idhotel_country_codehotel_live_datehotel_star_ratingaccommadation_type_namecharge_optionh_customer_idcustomer_nationalityguest_is_not_the_customerguest_nationality_country_nameno_of_adultsno_of_childrenno_of_extra_bedno_of_roomorigin_country_codelanguageoriginal_selling_amountoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_incancellation_policy_codeis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckinhotel_area_codehotel_brand_codehotel_chain_codehotel_city_codecancellation_datetime
078614452589186089622017-10-21 19:26:002018-07-31 00:00:002018-08-01 00:00:0088838AU2012-10-04 10:03:004.0ResortPay Now5310397980746630019China0China12002CNS.Chinese / Mainland436.52AlipayCredit CardCNYTrue1D1N_1NFalse1.00.00.00.00.00.00.0583250.0675.0461NaN
1-31759251106161709192018-08-10 15:40:002018-08-10 00:00:002018-08-11 00:00:0051920JP2012-10-01 10:03:003.0HotelPay Now3627650004083420090Japan0Japan1001JPJapanese54.01American ExpressCredit CardJPYTrue3D1N_1NFalseNaNNaNNaNNaNNaNNaNNaN643296.0191.02249NaN
2-51662000420283805172018-09-14 21:56:002018-09-14 00:00:002018-09-15 00:00:0090189TW2014-06-12 08:05:004.0HotelPay Now6891579066921110017Taiwan0Taiwan2001TWT. Chinese / Taiwan99.18VisaCredit CardTWDTrue1D1N_1NFalseNaNNaNNaNNaNNaNNaNNaN2900NaNNaN892NaN
361652112785008495662018-09-13 13:34:002018-09-13 00:00:002018-09-14 00:00:00236389TR2010-08-31 07:16:000.0Guest House / Bed & BreakfastPay Now5915665216707180023Turkey0Turkey1001TRTurkish19.36MasterCardCredit CardTRYFalse365D100P_100PFalse0.00.00.01.00.00.00.03110NaNNaN744NaN
4-18530921314209735672018-02-01 20:44:002018-08-31 00:00:002018-09-02 00:00:00187085JP2010-07-01 07:38:003.5HotelPay Later8271660960620410074South Korea0South Korea1001KRKorean175.52VisaCredit CardKRWTrue3D100P_100PFalse0.00.00.00.00.00.00.03760453.0359.022602018-02-02
584789739241734153542018-06-21 23:14:002018-07-15 00:00:002018-07-17 00:00:00788472TH2015-01-15 15:54:003.0HotelPay Now1760275406207060063South Korea0South Korea2001KRKorean68.38UnionPay - CreditcardCredit CardKRWFalse365D100P_100PFalseNaNNaNNaNNaNNaNNaNNaN1709NaNNaN1636NaN
6-18534377993681625802018-07-27 07:23:002018-08-19 00:00:002018-08-21 00:00:001634464TH2016-12-19 12:24:003.5HotelPay Later4700808392292780093South Korea0South Korea1001KREnglish163.60VisaCredit CardKRWTrue1D1N_100PFalse1.00.00.00.00.00.01.02964442.0600.024772018-08-17
72593597287179571502018-07-24 19:59:002018-08-10 00:00:002018-08-13 00:00:003002333TH2017-11-11 20:57:002.0HotelPay Later3622815626917240092Thailand0Thailand2001THThai48.54VisaCredit CardTHBFalse1D1N_1NFalse1.00.00.01.00.00.00.02964NaNNaN2477NaN
8-16245881242607000232018-08-25 06:39:002018-09-15 00:00:002018-09-17 00:00:001160850KR2016-01-27 12:39:003.0HotelPay Now3580464825046080007South Korea0South Korea3001KRKorean119.11UnionPay - CreditcardCredit CardKRWFalse7D100P_100PTrue1.00.00.00.01.00.00.03901NaNNaN24592018-08-25
987034856998983293352018-04-23 23:19:002018-08-18 00:00:002018-08-19 00:00:00304893FJ2010-11-17 07:09:003.5HotelPay Now2129668180375770076China0China2001CNS.Chinese / Mainland87.78UNKNOWNInvoiceCNYFalse7D50P_2D100P_100PTrueNaNNaNNaNNaNNaNNaNNaN4806NaNNaN2745NaN
h_booking_idbooking_datetimecheckin_datecheckout_datehotel_idhotel_country_codehotel_live_datehotel_star_ratingaccommadation_type_namecharge_optionh_customer_idcustomer_nationalityguest_is_not_the_customerguest_nationality_country_nameno_of_adultsno_of_childrenno_of_extra_bedno_of_roomorigin_country_codelanguageoriginal_selling_amountoriginal_payment_methodoriginal_payment_typeoriginal_payment_currencyis_user_logged_incancellation_policy_codeis_first_bookingrequest_nonesmokerequest_latecheckinrequest_highfloorrequest_largebedrequest_twinbedsrequest_airportrequest_earlycheckinhotel_area_codehotel_brand_codehotel_chain_codehotel_city_codecancellation_datetime
46917-39553707078476277562018-05-27 12:46:002018-07-05 00:00:002018-07-08 00:00:00289317HK2011-10-26 16:50:001.0HostelPay Now2223633554451910061China0China2001CNS.Chinese / Mainland116.77WeChatCredit CardCNYTrue365D100P_100PFalseNaNNaNNaNNaNNaNNaNNaN5262NaNNaN142NaN
46918-72802540025534940062018-03-21 18:23:002018-09-27 00:00:002018-09-28 00:00:00196931JP2011-06-29 10:05:004.0HotelPay Later3057302628167480032South Korea0South Korea1001KRKorean196.43MasterCardCredit CardKRWTrue3D1N_1NFalseNaNNaNNaNNaNNaNNaNNaN272287.0103.013352018-03-30
4691980388618491940519672018-02-01 20:34:002018-08-31 00:00:002018-09-02 00:00:00812762JP2015-03-11 14:43:003.0HotelPay Later8271660960620410074South Korea0South Korea1001KRKorean132.78VisaCredit CardKRWFalse28D100P_100PTrue0.00.00.00.00.00.00.03760NaNNaN22602018-02-02
46920-56878484890977468532018-06-29 20:17:002018-08-03 00:00:002018-08-06 00:00:00868632JP2015-02-26 16:35:001.0HostelPay Now989627699560000000South Korea0South Korea1001KRKorean87.01VisaCredit CardKRWTrue2D50P_100PFalseNaNNaNNaNNaNNaNNaNNaN3950NaNNaN2202018-07-03
4692148051481419012198622018-06-22 06:21:002018-09-02 00:00:002018-09-04 00:00:003132844KR2017-11-06 16:47:003.0HotelPay Now4543871625495590025Taiwan0Taiwan3001TWT. Chinese / Taiwan183.54VisaCredit CardTWDTrue3D100PFalse0.00.00.00.00.00.00.05242NaNNaN731NaN
4692278593615562332001642018-09-15 22:29:002018-09-16 00:00:002018-09-17 00:00:002504240JP2017-07-08 14:23:003.0RyokanPay Now2856938804428580064Japan0Japan2001JPJapanese142.68UNKNOWNCredit CardJPYFalse2D100PTrue1.00.00.01.00.00.00.0185NaNNaN2039NaN
4692327789414686847586002018-09-22 22:42:002018-09-27 00:00:002018-09-29 00:00:002613173JP2017-08-15 07:07:002.0Guest House / Bed & BreakfastPay Now8706898603750380065South Korea0South Korea1001KRKorean49.74MasterCardCredit CardKRWTrue1D50P_100PFalseNaNNaNNaNNaNNaNNaNNaN4463NaNNaN25672018-09-22
46924-89527943036527181612018-04-05 22:41:002018-07-22 00:00:002018-07-24 00:00:00276008KR2014-06-19 08:05:003.5HotelPay Later2011401455184750047Taiwan0Taiwan3001TWT. Chinese / Taiwan140.12VisaCredit CardTWDTrue7D50P_2D100P_100PFalseNaNNaNNaNNaNNaNNaNNaN1240188.0454.07312018-04-05
46925-42863049013160776292018-07-31 13:00:002018-08-01 00:00:002018-08-03 00:00:001573295TW2016-10-04 13:32:003.0HotelPay Now7713317830746380044Taiwan1Taiwan2001TWT. Chinese / Taiwan87.30MasterCardCredit CardTWDTrue2D100PFalse1.00.00.00.00.00.00.05208NaNNaN2004NaN
4692685160144713308046252018-07-26 15:50:002018-08-29 00:00:002018-08-30 00:00:0091737IN2009-06-28 02:02:003.5HotelPay Later2430631550982440067Bangladesh1Bangladesh2001BDEnglish58.01MasterCardCredit CardUSDTrue2D1N_1NFalse0.00.00.01.00.00.00.02597NaNNaN2302NaN